AI in Finance Summit Chicago schedule

•    Why PE firms have a structural AI advantage that most haven't yet operationalized, and how the constraints of smaller portfolio companies force a more pragmatic, outcome-first approach to AI deployment
•    From raw portfolio data to decision intelligence: building an AI layer across a portfolio without enterprise-scale resources or a dedicated data engineering team
•    Measuring AI ROI in a PE context: how to connect AI investment to hold period value creation, not just internal efficiency

 

 

Enterprise conversational AI has moved through three eras: rule-based NLU that worked but couldn’t scale, the generative leap that changed what was possible overnight, and now the agentic shift, where AI doesn’t just understand the customer, it decides and acts. In financial services, the window to lead that shift instead of reacting to it is closing fast. In this session, Lanier Pegues (NiCE Cognigy) traces that evolution and unpacks what it takes to move from conversation-driven applications to a governed, agentic operating layer that’s built to last.
•    Why this moment is a platform shift, not an upgrade cycle — and what “future-proofed” requires in a regulated, telephony-first environment
•    The disintermediation risk most finance leaders are underwriting by accident: what happens when your customer’s AI agent replaces their browser session
•    Moving from point solutions to orchestration — coordinating AI agents, human agents, and backend systems on a single data foundation
•    Governance as the enterprise gate: not AI unleashed, but AI that’s supervised, accountable, and audit-ready — the difference between a pilot and a deployment

 

 

A single AI agent tackling a complex workflow hits the same bottleneck as a single engineer owning an entire system- it's slow, fragile, and doesn't scale. This talk makes the case for multi-agent architectures where specialized agents run in parallel, each owning a discrete subtask, coordinated by an orchestration layer. We'll cover how to decompose workflows into parallelizable units, manage inter-agent communication and dependencies, and handle failures gracefully.

•    What are the most effective strategies to scale AI initiatives while keeping costs under control?
•    How can organizations balance the transformative potential of generative AI with the risks it introduces?
•    In what ways AI Agents are reshaping business operations, and what challenges come with their adoption?
•    How do you measure and ensure tangible business value from AI investments across the enterprise?

Panelists:
Panelists:
Hunter Treschl, Manager, AI Center of Excellence, CBOE GLOBAL MARKETS
Karthikcharan Suresh, Sr. Lead Data & AI, HIGHSTREET INSURANCE PARTNERS
Kishan Kumar, Senior Software Engineer - Data and AI, CHIME
Kelly Marie O'Brien, Vice President, Innovation Economy Card, JPMORGANCHASE

Moderator: Matthew Phoenix, Head Americas - Financial Services, IMPETUS


 

•    Institutional AI Challenges: Although large language models (LLMs) can generate market insights efficiently, issues such as hallucinations and unverifiable statements render them unsuitable for regulated production environments.
•    Architectural Innovation: Harbor Capital Advisors has developed a Multi-Agent Act-Verify-Refine (AVR) framework—a self-correcting, validation-focused system designed to surpass traditional one-shot generation methods, achieving institutional-grade reliability.
•    Self-Correcting Mechanism: Dedicated agents systematically dissect narratives into individual claims, validate each assertion, and refine results iteratively until regulatory requirements are fully satisfied.
•    Key Recommendations: Establish agentic state machines centered on validation, incorporate automated claim auditing, ensure robust data grounding, and delineate specific Human-in-the-Loop processes to maintain subject matter expertise while ensuring scalability.

 

•    How can financial institutions ensure the quality, integrity, and governance of customer data to drive reliable AI outcomes?
•    In what ways does trusted customer data accelerate the development of innovative, personalized financial products and services?
•    What are the biggest challenges in balancing customer privacy with the need for data to fuel AI innovation?

Panelists:
Alan Zhang, SVP Credit Analytics & Infrastructure, FIRST CITIZENS BANK
Goutham Miryala, Sr. Data Engineer, CHIME
Will Johnson, Head of Data Engineering and Development, CERITY PARTNERS
Jubinder Singh, Senior Manager, Corporate Strategy AI, CHARLES SCHWAB

Moderator: Dian Xu, VP of Engineering in Data, NEW AMERICAN FUNDING 

•    Bridging the gap between data teams and business leadership
•    Enabling scalable AI through cross-functional collaboration
•    Using emotional intelligence to lead change and foster adoption
•    Positioning soft skills as a strategic advantage in AI-driven initiatives

• How AI is evolving from prediction to execution (AI agents in trading, risk, and operations)
• What “agentic AI” means for banks, hedge funds, and fintechs
• The future role of humans in AI-driven financial decisions

•    Transforming MDM with autonomous AI agents 
•    Improving data quality and governance through intelligent automation 
•    Enabling trusted, scalable AI with unified enterprise data 

•    Why today’s semantic layer is breaking and silently corrupting both analytics and AI
•    How we built a deterministic, versioned semantic control plane (no LLM guesswork)
•    Turning semantics into infrastructure that powers both BI and GenAI consistently

•    How does poor data quality directly undermine AI outcomes in finance?
•    What governance practices truly build trust in AI-driven decisions?
•    How can institutions balance innovation with strict data controls?
•    What lessons from early adopters show the ROI of strong data governance?